Two essential factors of a social network are users and events. The users can follow each other to share information. When a new event occurs on the social network, a user may respond in a manner of sharing the event with friends or submitting a comment, so as to spread information, and finally achieve a stable state, that is, a final state of the user regarding an event may be an activated state (sharing or commenting on the event), or a non-activated state.
At the beginning after generation of a new event, only some users respond, and states of most users are unknown. How to predict a final state of a user as early as possible is a technology that has important practical value. In a conventional technology, generally, it is assumed that a state of a user is quickly converged to one of the following two states: an activated state or a non-activated state. In the prior art, it has been considered that sometimes a time used by a user to respond to an event is excessively long, and an asynchronous time delay independent cascade model (AsIC model) and an asynchronous time delay linear threshold model (AsLT model) are mainly used for analysis.
For a conventional AsIC model, nodes on a social network only have an activated state and a non-activated state, and the nodes can only change from a non-activated state to an activated state. Assuming that at a time t, a neighboring node w of a node v becomes an activated node, a probability that the neighboring node w enables the node v in a non-activated state to change to an activated state is pv,w. If there are multiple activated neighboring nodes around the node v, an order in which the neighboring nodes affect the node v is arbitrary, but an affecting degree is related to a value of pv,w, and a larger value of pv,w indicates that the node v is more likely to be activated. After being activated, the node v also affects neighboring nodes of the node v. Such a process is repeated until there is no node that can be activated. For the AsLT model, nodes on a social network have only an activated state and a non-activated state, and the nodes can only change from a non-activated state to an activated state. Each node v has a threshold θv, where θv∈[0,1], which represents a difficulty level at which the node v is affected, and a smaller threshold indicates that the node v is more likely to be activated. A set of neighboring nodes that affect the node v is N(v), and for any w∈N(v), bv,w represents a degree at which a node w affects the node v, and satisfies s(v)=Σwbv,w≤1. If s(v)≥θv, the node v changes to an activated state; and w refers to a node in an activated state in N(v). After being activated, the node v also affects neighboring nodes of the node v. Such a process is repeated until there is no node that can be activated.
In the prior art, a relationship of mutual attraction between a user and an event is not fully considered. That is, for an event that a user is interested in, the user generally responds quickly; for an event that the user is not interested in at all, eventually, the user usually does not make any response; and for an event between the two types, the user usually enters a swinging or silent state. Besides, some phenomena of social psychology on a social network are not considered, either. For example, if a total quantity of associated users in an activated state increases, a probability that the user changes to an activated state generally also increases accordingly. Therefore, in the prior art, at the beginning after generation of a new event, it cannot be fully considered that users may be in a state between an activated state and a non-activated state, and final states of a large quantity of users cannot be inferred precisely under full and comprehensive consideration of factors that may affect the states of the users, so that the states of the users cannot be accurately and precisely monitored.